System and method for providing a disability insurance claim triage platform

ABSTRACT

According to some embodiments, a triage platform may receive data indicative of a disability insurance claim submitted in connection with a disability insurance policy, including at least one claim characteristic. The triage platform may determine, based on the claim characteristic, a claim segment to be associated with the disability insurance claim. A claim handler may be assigned to the disability insurance claim in accordance with the determined claim segment. Information about the disability insurance claim may then be automatically routed to the assigned claim handler.

FIELD

The present invention relates to computer systems and more particularlyto computer systems that provide a disability insurance claim triageplatform.

BACKGROUND

An insurer may provide payments when claims are made in connection witha disability insurance policy. For example, an employee who becomes tooill to work might receive payments associated with a long termdisability insurance policy purchased by his or her employer. Note thatpayments might continue until the employee is able to return to work.The insurer may assign a claim handler to communicate with the employee,the employer, and/or medical service providers to facilitate theemployee's return to the workplace. Moreover, different claim handlersmay have different abilities and/or different workloads.

In one approach, newly received disability insurance claims might beassigned to claim handlers in a random or round robin manner. This,however, might lead to one claim handler having a significantly morecomplex workload as compared to another claim handler. To avoid such aresult, particular types of disability insurance claims might be moreeffectively assigned to particular claim handlers. For example, arelatively complicated insurance claim might be more efficientlyprocessed by a claim handler who handles a relatively small number ofinsurance claims and/or is especially skilled when it comes to handlingthese types of insurance claims.

Manually determining which claim handler should be assigned to eachindividual insurance claim, however, can be time consuming task,especially when there are a substantial number of claims to be analyzed.For example, an insurer might receive tens of thousands of new long termdisability insurance claims each year (which might represent a billiondollars of potential liability). It would therefore be desirable toprovide systems and methods to facilitate the assignment of disabilityinsurance claims to claim handlers, in an automated, efficient, andaccurate manner.

SUMMARY

According to some embodiments, systems, methods, apparatus, computerprogram code and means may facilitate the assignment of disabilityinsurance claims to claim handlers. In some embodiments, a triageplatform may receive data indicative of a disability insurance claimsubmitted in connection with a disability insurance policy, including atleast one claim characteristic. The triage platform may determine, basedon the claim characteristic, a claim segment to be associated with thedisability insurance claim. A claim handler may be assigned to thedisability insurance claim in accordance with the determined claimsegment. Information about the disability insurance claim may then beautomatically routed to the assigned claim handler.

Some embodiments provide: means for receiving, at a triage platform,data indicative of a disability insurance claim submitted in connectionwith a disability insurance policy, including at least one claimcharacteristic; means for determining, by a computer processor of thetriage platform based on the claim characteristic, a claim segment to beassociated with the disability insurance claim; means for assigning aclaim handler to the disability insurance claim in accordance with thedetermined claim segment; and means for automatically routinginformation about the disability insurance claim to the assigned claimhandler.

A technical effect of some embodiments of the invention is an improvedand computerized method to facilitate the assignment of disabilityinsurance claims to claim handlers. With these and other advantages andfeatures that will become hereinafter apparent, a more completeunderstanding of the nature of the invention can be obtained byreferring to the following detailed description and to the drawingsappended hereto.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is block diagram of a system according to some embodiments of thepresent invention.

FIGS. 2 and 3 illustrate methods that might be performed in accordancewith some embodiments.

FIG. 4 is block diagram of a triage tool or platform according to someembodiments of the present invention.

FIG. 5 is a tabular portion of a long term disability insurance claimdatabase according to some embodiments.

FIG. 6A illustrates a disability insurance claim system input inaccordance with some embodiments.

FIG. 6B illustrates a triage platform graphical user output inaccordance with some embodiments.

FIG. 7 is a long term disability insurance claim process flow inaccordance with some embodiments.

FIG. 8 is a partially functional block diagram that illustrates aspectsof a computer system provided in accordance with some embodiments of theinvention.

FIG. 9 is a block diagram that provides another representation ofaspects of the system of FIG. 8.

FIG. 10 is a flow chart illustrating how a predictive model might betrained according to some embodiments.

FIG. 11 illustrates predictive model inputs according to someembodiments.

DETAILED DESCRIPTION

An insurer may provide payments when claims are made in connection witha “disability insurance” policy. As used herein, the phrase “disabilityinsurance” may refer to a form of long term disability insurance thatinsures an employee's earned income against the risk that a disabilitywill prevent him or her from performing core work functions. Forexample, the inability to lift heavy objects or maintain focus (as witha psychological disorder), an illness or other conditions may causephysical impairment and an inability to work. Insurance paymentsgenerally continue until the employee is able to return to work, and inmany cases the insurer will assign a claim handler to communicate withthe employee, the employer, and/or medical service providers tofacilitate the employee's return to the workplace. Note that embodimentsmay also be associated with other types of disability insurance,including workers' compensation insurance, short term disabilityinsurance, and/or flexible combinations of short and long termdisability insurance.

In some cases, different claim handlers will have different abilitiesand/or different workloads. As a result, particular types of disabilityinsurance claims might be more effectively assigned to particular claimhandlers. Manually determining which claim handler should be assigned toeach individual insurance claim, however, can be time consuming anddifficult task, especially when there are a substantial number of claimsto be analyzed. It would therefore be desirable to provide systems andmethods to facilitate the assignment of disability insurance claims toclaim handlers. FIG. 1 is block diagram of a system 100 according tosome embodiments of the present invention. In particular, the system 100includes a triage platform 150 that receives information aboutdisability insurance claims (e.g., by receiving an electronic file froma team leader, an employer, an employee, an insurance agent, a medicalservice provider, or a data storage 110). According to some embodiments,incoming telephone calls and/or documents from a doctor may be used tocreate information in a claim system 120 which, in turn, can provideinformation to the triage platform 150. In other embodiments, the triageplatform 150 may retrieve information from a data warehouse 130 (e.g.,when the triage platform 150 is associated with a long term disabilityinsurance system, some information may be copied from a short termdisability system data warehouse). In other embodiments, some or all ofthe information about a disability claim may be received via an onlyclaim submission process. The triage platform may, according to someembodiments, provide an automatic initial assessment of new insuranceclaim to determine an appropriate claim segment based on complexityand/or identify a particular claim handler 160 to process the insuranceclaim. According to some embodiments, recovery profile information maybe generated and provided to claim handler. For example, historicalinformation may be used to generate appropriate recovery profileinformation based on the specific facts of the insurance claim beingprocessed.

The triage platform 150 might be, for example, associated with aPersonal Computers (PC), laptop computer, an enterprise server, a serverfarm, and/or a database or similar storage devices. The triage platform150 may, according to some embodiments, be associated with an insuranceprovider.

According to some embodiments, an “automated” triage platform 150 mayfacilitate the assignment of disability insurance claims to claimhandlers 160. For example, the triage platform 150 may automaticallyoutput a recommended claim segment for a received insurance claim (e.g.,to a team leader) which may then be used to facilitate assignment of aclaim handler 160. As used herein, the term “automated” may refer to,for example, actions that can be performed with little (or no)intervention by a human.

As used herein, devices, including those associated with the triageplatform 150 and any other device described herein, may exchangeinformation via any communication network which may be one or more of aLocal Area Network (LAN), a Metropolitan Area Network (MAN), a Wide AreaNetwork (WAN), a proprietary network, a Public Switched TelephoneNetwork (PSTN), a Wireless Application Protocol (WAP) network, aBluetooth network, a wireless LAN network, and/or an Internet Protocol(IP) network such as the Internet, an intranet, or an extranet. Notethat any devices described herein may communicate via one or more suchcommunication networks.

The triage platform 150 may store information into and/or retrieveinformation from the data storage 110. The data storage 110 might beassociated with, for example, a client, an employer, or insurance policyand might store data associated with past and current disabilityinsurance claims. The data storage 110 may be locally stored or resideremote from the insurance claim triage platform 150. As will bedescribed further below, the data storage 110 may be used by the triageplatform 150 to generate predictive models. According to someembodiments, the triage platform 150 communicates a recommended claimsegment, such as by transmitting an electronic file to a claim handler160, a client device, an insurance agent or analyst platform, an emailserver, a workflow management system, etc. In other embodiments, thetriage platform 150 might output a claim segment indication to a teamleader who might select a claim handler based on that indication oroverride the indication based on other factor associated with thedisability claim.

Although a single triage platform 150 is shown in FIG. 1, any number ofsuch devices may be included. Moreover, various devices described hereinmight be combined according to embodiments of the present invention. Forexample, in some embodiments, the claim triage platform 150 and datastorage 110 might be co-located and/or may comprise a single apparatus.

FIG. 2 illustrates a method that might be performed by some or all ofthe elements of the system 100 described with respect to FIG. 1according to some embodiments of the present invention. The flow chartsdescribed herein do not imply a fixed order to the steps, andembodiments of the present invention may be practiced in any order thatis practicable. Note that any of the methods described herein may beperformed by hardware, software, or any combination of these approaches.For example, a computer-readable storage medium may store thereoninstructions that when executed by a machine result in performanceaccording to any of the embodiments described herein.

At 202, data indicative of a disability insurance claim submitted inconnection with a disability insurance policy may be received. The dataindicative of the disability insurance claim might be received, forexample, via a submitted paper claim or a telephone call center. Thereceived data may include at least one claim “characteristic” associatedwith the insurance claim. Examples of claim characteristics include,without limitation, an employee's date of birth, a date of disability, awaiting period (e.g., how long a claimant might need to be unable towork before payments are provided), diagnosis information, a claimantsalary, an own occupation period (e.g., after which an employee mightreturn to a different type of job), a job type, a marriage status, abenefit percentage, claimant gender, and/or information from anattending physician.

At 204, a claim “segment” to be associated with the disability insuranceclaim may be determined. For example, potential claim segments mightinclude a segment for higher complexity disability insurance claims anda segment for lower complexity disability insurance claims.

At 206, a claim handler is assigned to the disability insurance claim inaccordance with the determined claim segment. Note that otherinformation may also be considered when determining a claim segmentand/or claim handler. For example, the language spoken by the claimantand/or the proficiency and/or specialized abilities of a claim handlermight be taken into account. Information about the disability insuranceclaim may then be automatically routed to the assigned claim handler at208. According to some embodiments, the system may further output anindication of the determined claim segment to a team leader, determineand output an indication of a diagnosis description for the disabilityinsurance claim, and/or determine and outputting a recovery profile forthe disability insurance claim. For example, the system may transmit, tothe assigned claim handler, recovery profile comments generated based onhistorical results and the details regarding the claim being handled.Note that a plurality of recovery profiles might be provided, eachassociated with a different recovery period.

The disability insurance claim might be associated with a particularclaim segment in accordance with any number of various business logicrules. For example, FIG. 3 illustrates one method that might beperformed by some or all of the elements of the system 100 describedwith respect to FIG. 1 according to some embodiments of the presentinvention. In this example, the insurance policy is a long termdisability insurance policy and three segments have been established:

-   -   Segment One: for claims which may require less intense        intervention (which can be assigned to claim handlers with        relatively heavy workloads);    -   Segment Two: for typical claims which may have a possible return        to work in either the claimant's own occupation or an alternate        occupation; and    -   Segment Three: for claims which may have medical, occupational,        and/or financial complexities which require more in-depth        investigation (which should be assigned to claim handlers with        relatively light workloads so they can devote more time to each        individual insurance claim or to claim handlers who are skilled        at handling complex claims).

At 302, data indicative of a long term disability insurance claimsubmitted in connection with a long term disability insurance policy maybe received. At 304, the insurance claim is evaluated to determine ifthe claim meets at least two of the following three criteria: (1) is ita relatively subjective diagnosis (e.g. clinical depression, Lymedisease), (2) is there an “uncertain” recovery profile, and (3) is therea greater than average amount of financial complexity associated withthe claim. If at least two of those criteria are present at 304, theinsurance claim is associated with segment three at 306 (for highercomplexity long term disability insurance claims).

If the insurance claim did not have at least two of the criteria at 304,it is determined at 308 whether or not the insurance claim's recoveryprofile satisfies either: (1) a likelihood of recovery above apre-determined threshold value (e.g., a pregnant employee is very likelyto return to work) or (2) a likelihood of recovery below apre-determined threshold (e.g., an employee with late stage pancreaticcancer is very unlikely to return to work). If either of theseconditions are true at 308, the insurance claim is associated withsegment one at 310 (for lower complexity long term disability insuranceclaims), otherwise the insurance claim is associated with segment two at312. The determined segment might then be output (e.g., to a teamleader) and/or used to assign a claim handler to the insurance claim.

The embodiments described herein may be implemented using any number ofdifferent hardware configurations. For example, FIG. 4 illustrates atriage platform 400 that may be, for example, associated with the system100 of FIG. 1. The triage platform 400 comprises a processor 410, suchas one or more commercially available Central Processing Units (CPUs) inthe form of one-chip microprocessors, coupled to a communication device420 configured to communicate via a communication network (not shown inFIG. 4). The communication device 420 may be used to communicate, forexample, with one or more remote team leader and/or claim handlerdevices. The triage platform 400 further includes an input device 440(e.g., a mouse and/or keyboard to enter information about an insuranceclaim and/or segmentation logic) and an output device 450 (e.g., tooutput a recommended segment and/or claim handler).

The processor 410 also communicates with a storage device 430. Thestorage device 430 may comprise any appropriate information storagedevice, including combinations of magnetic storage devices (e.g., a harddisk drive), optical storage devices, mobile telephones, and/orsemiconductor memory devices. The storage device 430 stores a program412 and/or a triage engine application 414 for controlling the processor410. The processor 410 performs instructions of the programs 412, 414,and thereby operates in accordance with any of the embodiments describedherein. For example, the processor 410 may receive data indicative of along term disability insurance claim submitted in connection with a longterm disability insurance policy, including at least one claimcharacteristic. The processor 410 may determine, based on the claimcharacteristic, a claim segment to be associated with the long termdisability insurance claim. A claim handler may be assigned to the longterm disability insurance claim by the processor 410 in accordance withthe determined claim segment. Information about the long term disabilityinsurance claim may then be automatically routed to the assigned claimhandler by the processor 410.

The programs 412, 414 may be stored in a compressed, uncompiled and/orencrypted format. The programs 412, 414 may furthermore include otherprogram elements, such as an operating system, a database managementsystem, and/or device drivers used by the processor 410 to interfacewith peripheral devices.

As used herein, information may be “received” by or “transmitted” to,for example: (i) the triage platform 400 from another device; or (ii) asoftware application or module within the triage platform 400 fromanother software application, module, or any other source.

In some embodiments (such as shown in FIG. 4), the storage device 430further stores insurance claim data 500, a claim handler data 460 (e.g.,indicating a handlers workload, experience, special expertise, etc.),and segment rules 470. An example of a database that may be used inconnection with the triage platform 400 will now be described in detailwith respect to FIG. 5. Note that the database described herein is onlyone example, and additional and/or different information may be storedtherein. Moreover, various databases might be split or combined inaccordance with any of the embodiments described herein. For example,the claim handler data 460 and/or segment rules 470 might be combinedand/or linked to each other within the triage engine application 414.

Referring to FIG. 5, a table is shown that represents the claim handlerdatabase 500 that may be stored at the triage platform 400 according tosome embodiments. The table may include, for example, entriesidentifying insurance claims submitted under a particular long termdisability insurance policy or a number of different policies. The tablemay also define fields 502, 504, 506, 508, 510 for each of the entries.The fields 502, 504, 506, 508, 510 may, according to some embodiments,specify: a claim identifier 502, a disability description 504, a date ofbirth 506, a determined segment 508, and an assigned claim handleridentifier 510. The claim handler database 500 may be created andupdated, for example, based on information electrically received and/ormanually entered into the system by a team leader.

The claim identifier 502 may be, for example, a unique alphanumeric codeidentifying a claim submitted in connection with a long term disabilityinsurance policy. The disability description 504 may indicate adiagnosis associated with the claim identifier 502 and the data of birth506 may indicate when he or she was born. Although two claimcharacteristics 504, 506 are illustrated in FIG. 5 for clarity, notethat an actual implementation may evaluate many other factors.

The determined segment 508 might be associated with an automaticallydetermined level of complexity assessed by a triage platform. Forexample, claim “C_(—)10001” was determined to be a segment one insuranceclaim because a pregnancy has a fairly predictable return to workrecovery profile (and may therefore be assigned to a claim handler witha relatively heavy workload). The determined segment 508 may then beused by a team leader and/or the triage platform to establish anassigned claim handler identifier 510. For example, claim handler“H_(—)101” might have a relatively heavy workload while claim handler“H_(—)103” has a relatively light workload (and can therefore devotemore time to each individual long term disability insurance claim).

By way of example, a team leader may enter key characteristic variablesfor a newly received long term disability insurance claim. Note that inthe case of a long term disability claim, some or all of thisinformation might be automatically populated based on corresponding dataelements of a prior associated short term disability claim. Responsiveto this entered information, the triage platform may provide arecommended claim segment based on the diagnosis, recovery profile,and/or the financial complexity of the claim. The triage platform mayalso output a diagnosis description pulled from the Official DisabilityGuidelines definition, and one or more recovery profiles. These factorsmay then be used by the team leader to an appropriate analyst resource.In addition, the recovery profile information may be used to help theteam leader understand the appropriate next follow-up actions for theinsurance claim as well as provide guidance that might need to beforwarded to the claim handler or analyst. According to someembodiments, a team leader might review and/or override a claim segmentthat was automatically determined by the triage platform.

FIG. 6A illustrates a disability insurance claim system input 602 inaccordance with some embodiments. The input 602 may include, forexample, text-based answers to questions 612 and/or selections from pulldown menus 622 to provide information about claimants and/or claimantconditions. According to some embodiments, answers to some questionsmight result in one or more follow-up questions being automaticallydetermined by the system and presented on the input 602. Informationprovided via the input 602 may, according to some embodiments, helpdetermine an appropriate segment and/or claim handler for a disabilityclaim.

FIG. 6B illustrates a triage platform graphical user output 600 thatmight be displayed to a team leader in accordance with some embodiments.The output 600 includes the recommended segment 610 (e.g., based on thedetermined segment 508 in the insurance claim database 500) along withthe reasons why that particular segment was determined (e.g., based onthe diagnosis, the recovery profile, and/or financial complexity of theclaim). According to some embodiments, the output 600 further includes adiagnosis description for the insurance claim. The diagnosis descriptionmight, for example, be pulled from the Official Disability Guidelinesdefinitions.

The output 600 also includes 6 month and 24 month recoveryrecommendations 620 for the insurance claim. The predicted 24 monthReturn To Work (“RTW”) recovery rate might be determined, for example,using the employee's particular medical condition and other factorsentered by the team leader. Note that the recovery recommendations 620,as well as the other information provided on the output, may begenerated by multiple predictive models (e.g., different modelsassociated with different time periods may output suggested text).

In addition, the output 600 includes an overall recovery profile 630.For example, a behavioral health recovery rate might be determined basedon the medical condition (e.g., clinical depression or bi-polardisorder), the elimination period, marriage status, benefit percentage,salary, and/or gender of the claimant.

The output 600 further includes information about occupational risks,test change outlooks, and other considerations 640 that may be relevantto a claim handler. The test change outlook might represent an overalllikelihood that a claimant, who has reached a test change point, willpass a test change requirement meeting an “any occupation” definition ofdisability. The occupational risk information might be based on, forexample, job class, claimant age, diagnosis and/or salary.

A graphical representation 650 of the recovery profiles and/or otherdata in comparison to an average long term disability claim may also beprovided in the output 600. For example, the likelihood of the claimantrecovering in 6 months in contrast with the baseline (or average) forclaimants within the first 6 months might be displayed along with thelikelihood of the claimant recovering in 6 to 24 months in contrast withthe baseline (or average) for claimants within 6 to 24 months of whenthe claim was submitted.

FIG. 7 is a long term disability insurance claim process flow 700 inaccordance with some embodiments. After a new long term disabilityinsurance claim is received by a team leader at 710, information aboutthe claim may be entered into a triage platform at 712. The triageoutputs at 714 may be stored into data storage at 740 for later analysisand/or retrieval. The triage outputs at 714 are evaluated at 716 todetermine if the insurance claim needs to be re-assigned to another teamleader (e.g., because of special considerations regarding the claim orthe leader). If the insurance claim does not need to be re-assigned, theleader assigns the claim at 718 and it is forwarded to an analyst at 730for further processing. If the insurance claim needs to be re-assigned,it is received by the alternate team leader at 720, who may determinethe claim segment at 722 and assign the long term disability claim at724.

Thus, embodiments may provide group benefit claims with automated longterm disability segmentation allowing improved claim, medical, and/orfinancial management of insurance claims. According to some embodiments,the application of specific claim variables, including diagnosis andother claim characteristics, as well insurer's historical claim recoveryinformation may let claims be aligned within three segments based onclaim management complexity. Note however, that two, four or moresegments might be provided instead. An ability analyst and/or claimhandler may be aligned by segment and receive claims best suited for hisor her skill set, which may further result in improved claim management.That is, long term disability segmentation may result in optimizedoutcomes for long term disability insurance claims through betterapplication of skilled resources to the right claims. The triageplatform may assist team leaders with claim assessment (and claimassignment to an appropriate resource) by providing a recommended claimsegment and assisting with the creation of an appropriate claimmanagement plan.

According to some embodiments, a determination of an appropriate claimsegment may be based at least in part on a predictive model trained withhistorical long term disability insurance claim information. Forexample, triage platform segmentation might be aided by data modeling,input from an insurer's claim subject matter experts, and analysis ofhistorical claim experience. The following are some variables that mightbe used by a predictive model to help identify a correct claim segmentfor a long term disability claim:

-   -   Date of Birth (age, with older claimants perhaps requiring more        time to return to work),    -   Date of Disability,    -   Waiting Period,    -   Diagnosis,    -   Salary,    -   Own Occupation Period (period of being disabled from claimant's        own occupation as compared to any occupation),    -   And Job Type (e.g., Sedentary or Non-sedentary),    -   Spouse,    -   Benefit Percentage, and    -   Gender.        According to some embodiments, the predictive mode utilizes high        level diagnosis groupings. For example, by analyzing an        insurer's historical data for high level diagnostic groups        (e.g., cancer, respiratory, musculoskeletal, circulatory, etc.),        predictive models may be created for each group to identify the        variables most likely to impact duration and claim outcomes.        Factors that may contribute to the complexity of claim        management, such as salary and definition of disability, might        also be considered. Note that different diagnosis groupings may        be associated with different sets and/or weights of relevant        factors. For example, depending on the high level diagnosis        grouping (e.g., cancer, respiratory illness), different        variables may be significant and/or relevant and the weightings        of common variables may be different.

In general, and for the purposes of introducing concepts of embodimentsof the present invention, a computer system may incorporate a“predictive model.” As used herein, the phrase “predictive model” mightrefer to, for example, any of a class of algorithms that are used tounderstand relative factors contributing to an outcome, estimate unknownoutcomes, discover trends, and/or make other estimations based on a dataset of factors collected across prior trials. Note that a predictivemodel might refer to, but is not limited to, methods such as ordinaryleast squares regression, logistic regression, decision trees, neuralnetworks, generalized linear models, and/or Bayesian models. Thepredictive model is trained with historical claim transaction data, andis applied to current claim transactions to determine how the currentclaim transactions should be handled by a long term disability insuranceprogram. Both the historical claim transaction data and datarepresenting the current claim transactions might include, according tosome embodiments, indeterminate data or information extracted therefrom.For example, such data/information may come from narrative and/ormedical text notes associated with a claim file.

Features of some embodiments associated with a predictive model will nowbe described by first referring to FIG. 8. FIG. 8 is a partiallyfunctional block diagram that illustrates aspects of a computer system800 provided in accordance with some embodiments of the invention. Forpresent purposes it will be assumed that the computer system 800 isoperated by an insurance company (not separately shown) for the purposeof referring certain claims to long term disability insurance claimhandlers as appropriate.

The computer system 800 includes a data storage module 802. In terms ofits hardware the data storage module 802 may be conventional, and may becomposed, for example, by one or more magnetic hard disk drives. Afunction performed by the data storage module 802 in the computer system800 is to receive, store and provide access to both historical claimtransaction data (reference numeral 804) and current claim transactiondata (reference numeral 806). As described in more detail below, thehistorical claim transaction data 804 is employed to train a predictivemodel to provide an output that indicates how a claim should by handledby a long term disability insurance program (e.g., segment assignments),and the current claim transaction data 806 is thereafter analyzed by thepredictive model. Moreover, as time goes by, and results become knownfrom processing current claim transactions, at least some of the currentclaim transactions may be used to perform further training of thepredictive model. Consequently, the predictive model may thereby adaptitself to changing patterns of long term disability insurance claims.

Either the historical claim transaction data 804 or the current claimtransaction data 806 might include, according to some embodiments,determinate and indeterminate data. As used herein and in the appendedclaims, “determinate data” refers to verifiable facts such as the dateof birth, age or name of a claimant or name of another individual or ofa business or other entity; a type of injury, accident, sickness, orpregnancy status; a medical diagnosis; a date of loss, or date of reportof claim, or policy date or other date; a time of day; a day of theweek; a vehicle identification number, a geographic location; and apolicy number.

As used herein and in the appended claims, “indeterminate data” refersto data or other information that is not in a predetermined formatand/or location in a data record or data form. Examples of indeterminatedata include narrative speech or text, information in descriptive notesfields and signal characteristics in audible voice data files.Indeterminate data extracted from medical notes might be associatedwith, for example, a prior injury, alcohol related co-morbidityinformation, drug related co-morbidity information, tobacco relatedco-morbidity information, arthritis related co-morbidity information,diabetes related co-morbidity information, and/or obesity relatedco-morbidity information.

The determinate data may come from one or more determinate data sources808 that are included in the computer system 800 and are coupled to thedata storage module 802. The determinate data may include “hard” datalike the claimant's name, date of birth, social security number, policynumber, address; the date of loss; the date the claim was reported, etc.One possible source of the determinate data may be the insurancecompany's policy database (not separately indicated). Another possiblesource of determinate data may be from data entry by the insurancecompany's claims intake administrative personnel.

The indeterminate data may originate from one or more indeterminate datasources 810, and may be extracted from raw files or the like by one ormore indeterminate data capture modules 812. Both the indeterminate datasource(s) 810 and the indeterminate data capture module(s) 812 may beincluded in the computer system 800 and coupled directly or indirectlyto the data storage module 802. Examples of the indeterminate datasource(s) 810 may include data storage facilities for document images,for text files (e.g., claim handlers' notes) and digitized recordedvoice files (e.g., claimants' oral statements, witness interviews, claimhandlers' oral notes, etc.). Examples of the indeterminate data capturemodule(s) 812 may include one or more optical character readers, aspeech recognition device (i.e., speech-to-text conversion), a computeror computers programmed to perform natural language processing, acomputer or computers programmed to identify and extract informationfrom narrative text files, a computer or computers programmed to detectkey words in text files, and a computer or computers programmed todetect indeterminate data regarding an individual. For example, claimhandlers' opinions may be extracted from their narrative text filenotes.

The computer system 800 also may include a computer processor 814. Thecomputer processor 814 may include one or more conventionalmicroprocessors and may operate to execute programmed instructions toprovide functionality as described herein. Among other functions, thecomputer processor 814 may store and retrieve historical claimtransaction data 804 and current claim transaction data 806 in and fromthe data storage module 802. Thus the computer processor 814 may becoupled to the data storage module 802.

The computer system 800 may further include a program memory 816 that iscoupled to the computer processor 814. The program memory 816 mayinclude one or more fixed storage devices, such as one or more hard diskdrives, and one or more volatile storage devices, such as RAM (randomaccess memory). The program memory 816 may be at least partiallyintegrated with the data storage module 802. The program memory 816 maystore one or more application programs, an operating system, devicedrivers, etc., all of which may contain program instruction steps forexecution by the computer processor 814.

The computer system 800 further includes a predictive model component818. In certain practical embodiments of the computer system 800, thepredictive model component 818 may effectively be implemented via thecomputer processor 814, one or more application programs stored in theprogram memory 816, and data stored as a result of training operationsbased on the historical claim transaction data 804 (and possibly alsodata resulting from training with current claims that have beenprocessed). In some embodiments, data arising from model training may bestored in the data storage module 802, or in a separate data store (notseparately shown). A function of the predictive model component 818 maybe to determine an appropriate complexity segment for current claimtransactions. The predictive model component may be directly orindirectly coupled to the data storage module 802.

The predictive model component 818 may operate generally in accordancewith conventional principles for predictive models, except, as notedherein, for at least some of the types of data to which the predictivemodel component is applied. Those who are skilled in the art aregenerally familiar with programming of predictive models. It is withinthe abilities of those who are skilled in the art, if guided by theteachings of this disclosure, to program a predictive model to operateas described herein.

Still further, the computer system 800 includes a model trainingcomponent 820. The model training component 820 may be coupled to thecomputer processor 814 (directly or indirectly) and may have thefunction of training the predictive model component 818 based on thehistorical claim transaction data 804. (As will be understood fromprevious discussion, the model training component 820 may further trainthe predictive model component 818 as further relevant claim transactiondata becomes available.) The model training component 820 may beembodied at least in part by the computer processor 814 and one or moreapplication programs stored in the program memory 816. Thus the trainingof the predictive model component 818 by the model training component820 may occur in accordance with program instructions stored in theprogram memory 816 and executed by the computer processor 814.

In addition, the computer system 800 may include an output device 822.The output device 822 may be coupled to the computer processor 814. Afunction of the output device 822 may be to provide an output that isindicative of (as determined by the trained predictive model component818) particular claim segments and/or claim handlers for the currentclaim transactions. The output may be generated by the computerprocessor 814 in accordance with program instructions stored in theprogram memory 816 and executed by the computer processor 814. Morespecifically, the output may be generated by the computer processor 814in response to applying the data for the current claim transaction tothe trained predictive model component 818. The output may, for example,be a true/false flag or a number within a predetermined range ofnumbers. In some embodiments, the output device may be implemented by asuitable program or program module executed by the computer processor814 in response to operation of the predictive model component 818.

Still further, the computer system 800 may include a routing module 824.The routing module 824 may be implemented in some embodiments by asoftware module executed by the computer processor 814. The routingmodule 824 may have the function of directing workflow based on theoutput from the output device. Thus the routing module 824 may becoupled, at least functionally, to the output device 822. In someembodiments, for example, the routing module may direct workflow byreferring, to a long term disability insurance program claim handler826, current claim transactions analyzed by the predictive modelcomponent 818 and found to be associated with a particular claimsegment. In particular, these current claim transactions may be referredto case manager 828 who is associated with the long term disabilityinsurance program claim handler 826. The long term disability insuranceprogram claim handler 826 may be a part of the insurance company thatoperates the computer system 800, and the case manager 828 might be anemployee of the insurance company.

FIG. 9 is another block diagram that presents a computer system 900 in asomewhat more expansive or comprehensive fashion (and/or in a morehardware-oriented fashion). The computer system 900, as depicted in FIG.9, includes a “triage platform” 901 given that a function of the triageplatform 901 is to automatically and selectively assess newly receivedlong term disability insurance claims for the insurance company. As seenfrom FIG. 9, the computer system 900 may further include a conventionaldata communication network 902 to which the triage platform 901 iscoupled.

FIG. 9 also shows, as parts of computer system 900, data input device(s)904 and data source(s) 906, the latter (and possibly also the former)being coupled to the data communication network 902. The data inputdevice(s) 904 and the data source(s) 906 may collectively include thedevices 808, 810 and 812 discussed above with reference to FIG. 8. Moregenerally, the data input device(s) 904 and the data source(s) 906 mayencompass any and all devices conventionally used, or hereafter proposedfor use, in gathering, inputting, receiving and/or storing informationfor insurance company claim files.

Still further, FIG. 9 shows, as parts of the computer system 900,personal computers 908 assigned for use by physicians (who may beassociated with the insurance company's long term disability insuranceprogram) and personal computers 910 assigned for use by case managers(who might also be associated with team leaders and/or claim handlersthe long term disability insurance program). The personal computers 908,910 are coupled to the data communication network 902.

Also included in the computer system 900, and coupled to the datacommunication network 902, is an electronic mail server computer 912.The electronic mail server computer 912 provides a capability forelectronic mail messages to be exchanged among the other devices coupledto the data communication network 902. Thus the electronic mail servercomputer 912 may be part of an electronic mail system included in thecomputer system 900. The computer system 900 may also be considered toinclude further personal computers (not shown), including, e.g.,computers which are assigned to individual claim handlers or otheremployees of the insurance company.

According to some embodiments, the triage platform 901 uses a predictivemodel to facilitate a provisioning of claim handlers. Note that thepredictive model might be designed and/or trained in a number ofdifferent ways. For example, FIG. 10 is a flow chart illustrating how apredictive model might be created according to some embodiments. At1002, data to be input to the predictive model may be analyzed,scrubbed, and/or cleaned. This process might involve a broad review ofthe relevant variables that may be included in the sample data.Variables might be examined for the presence of erroneous values, suchas incorrect data types or values that don't make sense. Observationswith such “noisy” data or missing data may be removed from the sample.Similarly, any data points that represent outliers are also managed.

At 1004, a data reduction process might be performed. This might occur,for example, between variables in the data sample and/or within specificvariables. According to some embodiments, certain variables may beassociated with one another and the number of these variables may bereduced. For example, it might be noted that injuries to the leftshoulder generally have values similar to injuries to the rightshoulder. Within certain variables, the raw values may represent a levelof information that is too granular. These raw values might then becategorized to reduce the granularity. A goal of the data reductionprocess may be to reduce the dimensionality of the data by extractingfactors or clusters that may account for the variability in the data.

At 1006, any necessary data transformations may be performed.Transformations of dependent and/or independent variables in statisticalmodels can be useful for improving interpretability, model fit, and/oradherence to modeling assumptions. Some common methods may includenormalizations of variables to reduce the potential effects of scale anddummy coding or other numeric transformations of character variables.

Once these steps are complete, the predictive model may be developed at1008. Depending on the nature of the desired prediction, variousmodeling techniques may be utilized and compared. The list ofindependent variables may be narrowed down using statistical methods aswell as business judgment. Lastly, the model coefficients and/or weightsmay be calculated and the model algorithm may be completed. For example,it might be determined that back injuries require a high degree ofmanagement (and thus, according to some embodiments, a back injury mightbe weighted more as compared to a shoulder injury and thus be morelikely to end up in a segment associated with claim handlers with lightworkloads).

Note that many different types of data might be used to create,evaluate, and/or use a predictive model. For example, FIG. 11 is a blockdiagram of a system 1100 illustrating inputs to a predictive model 1110according to some embodiments. In this example, the predictive model1110 might receive information about prior long term disabilityinsurance claims 1120 (e.g., historical data). Moreover, the predictivemodel 1110 might receive monetary information about claims 1130 (e.g., atotal amount of payments made in connection with a claim) and/ordemographic information 1140 (e.g., the age or sex of a claimant).According to some embodiments, claim notes 1150 are input to thepredictive model 1110 (e.g., and keywords may be extracted from thenotes 1150). Other types of information that might be provided to thepredictive model 1110 include medical bill information 1160 (e.g.,including information about medical care that was provided to aclaimant), disability details 1170 (e.g., which part or parts of thebody have been injured), and employment data 1180 (e.g., an employee'ssalary or how long an employee has worked for an employer).

The predictive model 1110, in various implementation, may include one ormore of neural networks, Bayesian networks (such as Hidden Markovmodels), expert systems, decision trees, collections of decision trees,support vector machines, or other systems known in the art foraddressing problems with large numbers of variables. Preferably, thepredictive model(s) are trained on prior data and outcomes known to theinsurance company. The specific data and outcomes analyzed varydepending on the desired functionality of the particular predictivemodel 1110. The particular data parameters selected for analysis in thetraining process are determined by using regression analysis and/orother statistical techniques known in the art for identifying relevantvariables in multivariable systems. The parameters can be selected fromany of the structured data parameters stored in the present system,whether the parameters were input into the system originally in astructured format or whether they were extracted from previouslyunstructured text.

The present invention has been described in terms of several embodimentssolely for the purpose of illustration. Persons skilled in the art willrecognize from this description that the invention is not limited to theembodiments described, but may be practiced with modifications andalterations limited only by the spirit and scope of the appended claims.

1. A system associated with a disability insurance policy, the systemcomprising: a computer storage unit for receiving, storing, andproviding data indicative of a disability insurance claim submitted inconnection with the disability insurance policy, including at least oneclaim characteristic; and a processor in communication with the storageunit, wherein the processor is configured for: determining, based on theclaim characteristic, a claim segment to be associated with thedisability insurance claim, assigning a claim handler to the disabilityinsurance claim in accordance with the determined claim segment, andautomatically routing information about the disability insurance claimto the assigned claim handler.
 2. The system of claim 1, wherein thedisability insurance claim comprises one of: (i) a long term disabilityinsurance claim, (ii) a short term disability insurance claim, or (iii)a workers' compensation insurance claim.
 3. The system of claim 1,wherein the disability insurance claim comprises a long term disabilityclaim and at least some of the data indicative of the long termdisability insurance claim is based on information copied from a shortterm disability insurance system.
 4. The system of claim 1, wherein theprocessor is further configured for: automatically transmittinginformation about the disability insurance claim to at least one of: (i)an email server, (ii) a workflow application, and (iii) a calendarapplication.
 5. The system of claim 1, wherein the computer storage unitreceives the data indicative of the disability insurance claim via atleast one of: (i) a submitted paper claim, and (ii) a telephone callcenter.
 6. The system of claim 1, wherein the claim characteristiccomprises at least one of: (i) a date of birth, (ii) a date ofdisability, (iii) a waiting period, (iv) diagnosis information, (v) aclaimant salary, (vi) an own occupation period, (vii) a job type, (viii)a marriage status, (ix) a benefit percentage, (x) claimant gender, and(xi) information from an attending physician.
 7. The system of claim 1,wherein potential claim segments include at least (i) a segment forhigher complexity disability insurance claims and (ii) a segment forlower complexity disability insurance claims.
 8. The system of claim 7,wherein the segment for higher complexity disability insurance claims isdetermined when at least two of the following conditions are true forthe disability insurance claim: (i) a subjective diagnosis is associatedwith the insurance claim, (ii) an uncertain recovery profile isassociated with the insurance claim, and (iii) a pre-determined level offinancial complexity is associated with the insurance claim.
 9. Thesystem of claim 7, wherein the segment for lower complexity disabilityinsurance claims is determined when either of following conditions istrue for the disability insurance claim: (i) a likelihood of recovery isbelow a first pre-determined threshold value or (ii) the likelihood ofrecovery is above a second pre-determined threshold value.
 10. Thesystem of claim 1, wherein the processor is further configured for:outputting an indication of the determined claim segment to a teamleader.
 11. The system of claim 1, wherein the processor is furtherconfigured for: determining and outputting an indication of a diagnosisdescription for the disability insurance claim.
 12. The system of claim1, wherein the processor is further configured for: determining andoutputting a recovery profile for the disability insurance claim basedon a predictive model.
 13. The system of claim 12, wherein a pluralityof recovery profiles are determined and output, each associated with adifferent recovery period.
 14. The system of claim 1, wherein theprocessor is further configured for: determining and outputting a testchange outlook for the disability insurance claim.
 15. The system ofclaim 1, wherein the determination is based at least in part on apredictive model trained with historical disability insurance claiminformation.
 16. The system of claim 15, wherein the predictive modeutilizes high level diagnosis groupings. 17-18. (canceled)
 19. A methodassociated with a long term disability insurance policy, the methodcomprising: receiving at a triage platform data indicative of a longterm disability insurance claim submitted in connection with the longterm disability insurance policy, including at least one claimcharacteristic; determining, by a computer processor of the triageplatform, based on the claim characteristic, a claim segment to beassociated with the long term disability insurance claim; assigning, bythe computer processor of the triage platform, a claim handler to thelong term disability insurance claim in accordance with the determinedclaim segment; and automatically routing, by the computer processor ofthe triage platform, information about the long term disabilityinsurance claim to the assigned claim handler.
 20. The method of claim19, wherein potential claim segments include at least (i) a segment forhigher complexity long term disability insurance claims and (ii) asegment for lower complexity long term disability insurance claims. 21.The method of claim 20, wherein the segment for higher complexity longterm disability insurance claims is determined when at least two of thefollowing conditions are true for the long term disability insuranceclaim: (i) a subjective diagnosis is associated with the insuranceclaim, (ii) an uncertain recovery profile is associated with theinsurance claim, and (iii) a pre-determined level of financialcomplexity is associated with the insurance claim.
 22. The method ofclaim 20, wherein the segment for lower complexity long term disabilityinsurance claims is determined when either of following conditions istrue for the long term disability insurance claim: (i) a likelihood ofrecovery is below a first pre-determined threshold value or (ii) thelikelihood of recovery is above a second pre-determined threshold value.23. A non-transitory, computer-readable medium storing instructionsadapted to be executed by a computer processor to perform a methodassociated with a long term disability insurance policy, said methodcomprising: receiving at a triage platform data indicative of a longterm disability insurance claim submitted in connection with the longterm disability insurance policy, including at least one claimcharacteristic; determining, by a computer processor of the triageplatform based on the claim characteristic, a claim segment to beassociated with the long term disability insurance claim; assigning aclaim handler to the long term disability insurance claim in accordancewith the determined claim segment; and automatically routing informationabout the long term disability insurance claim to the assigned claimhandler.
 24. The medium of claim 23, wherein potential claim segmentsinclude at least (i) a segment for higher complexity long termdisability insurance claims and (ii) a segment for lower complexity longterm disability insurance claims.
 25. The medium of claim 24, whereinthe segment for higher complexity long term disability insurance claimsis determined when at least two of the following conditions are true forthe long term disability insurance claim: (i) a subjective diagnosis isassociated with the insurance claim, (ii) an uncertain recovery profileis associated with the insurance claim, and (iii) a pre-determined levelof financial complexity is associated with the insurance claim.
 26. Themedium of claim 24, wherein the segment for lower complexity long termdisability insurance claims is determined when either of followingconditions is true for the long term disability insurance claim: (i) alikelihood of recovery is below a first pre-determined threshold valueor (ii) the likelihood of recovery is above a second pre-determinedthreshold value.